note to self: moments = statistical variales that can be determined. you also need to review the formulas for the expenctation valeus for univariate data
For this distribution, the parameter lambda and delta both are greater than 0. There is no upper bound on the possible values of the parameters. We just know they must be positive.
The second sample moment is defined as M_2 = sum (x_i^2) / n. which is correct in the video. The second sample moment here is (3^2 + 4^2 + 5^2 + 8^2) / 4
You are confusing the second sample moment with the population variance maybe? The variance would be a calculation such as the one you have. The kth sample moment is defined as the sum if the x_i raised to the kth power divided by the sample,
Thank you for explaining it so well! Very intuitive format
Thanks for the feedback and glad you found it helpful! Always nice to know people out there are benefiting.
many many thanks for the pure and easy explanation and examples
Thanks Ahmad!
Thank you ❤️
note to self: moments = statistical variales that can be determined. you also need to review the formulas for the expenctation valeus for univariate data
it was fabulous 😊❤❤🎉
Thanks for the kind words! Your welcome
Thank you so much! You saved my assignment!
which assignment did you have?
Why delta up to infinity is used?
For this distribution, the parameter lambda and delta both are greater than 0. There is no upper bound on the possible values of the parameters. We just know they must be positive.
The 2nd moment in your demonstration is wrong: it should be ( (3-5)^2 + (4-5)^2 + (5-5)^2 + (8-5)^2) / 4
The second sample moment is defined as M_2 = sum (x_i^2) / n. which is correct in the video. The second sample moment here is (3^2 + 4^2 + 5^2 + 8^2) / 4
You are confusing the second sample moment with the population variance maybe? The variance would be a calculation such as the one you have. The kth sample moment is defined as the sum if the x_i raised to the kth power divided by the sample,